André Sales Fonteles - A generic architecture and a recommendation strategy for spatial crowdsourcing platforms

Spatial Crowdsourcing Platforms (SCP) are systems that allow people, called requesters, to publish spatial tasks in order to find suitable workforce to perform it. These spatial tasks require workers to be at a given location, usually within a given time window, to be accomplished. Some examples of SCPs are: Uber, BlaBlaCar and TaskRabbit. SCPs are source of much interest for academy, however several research opportunities remain.
Doan et al. [2011] argued that the race is now on “toward building general crowdsourcing platforms that can be used to develop such systems quickly”. Since then, little has been done to investigate the technical design of SCPs precisely. Also, there is a gap between what is done in commercial platforms and in scientific literature. We propose GENIUS-C, a generic architecture for SCPs. We provide a reference implementation (RI) for GENIUS-C, that works as a framework for the development of SCPs. GENIUS-C and its RI are meant to fill the gap between the academic and industry world, and facilitate the understanding and the quick development of new SCPs.
We also study the important problem of matching workers and tasks. How can we find one or more tasks suitable for a worker (and vice versa)? Some tackle this issue using recommender system techniques, others optimization approaches. Most of them do not take into account the spatiotemporal dimensions of tasks and workers. Those who take it into account ignore the preferences of either workers, requesters or the system itself. In this context, we identify and model the following common real-life problem: once a worker is willing to spend sometime accomplishing tasks, what is the best sequence of tasks to be followed respecting their spatiotemporal constraints? How can this sequence be obtained taking into account the preferences of the worker, the requesters, the system itself, or a combination of them? We name this problem the Trajectory Recommendation Problem (TRP). After having proved its NP-hardness, we propose an exact algorithm to find optimal solutions to this problem, and compare it with several approximation heuristics.